I'm using MATLAB R2016a for binary classification (time series prediction) of a financial case. I have a good total accuracy (70~75%) but specificity is about 90% and sensitivity is about 60% and vice versa. Currently I'm using grid search to optimize my classification model (
neural network, etc.) based on total accuracy. My data-set has balanced samples of binary output.
How can I improve results in this case? Can I use any other performance metric to take into account unbalanced sensitivity and specificity (as mentioned, in some cases I have higher specificity than sensitivity and in some cases vice versa.)?